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2.905 skills encontradas
biopython
Primary Python toolkit for molecular biology. Preferred for Python-based PubMed/NCBI queries (Bio.Entrez), sequence manipulation, file parsing (FASTA, GenBank, FASTQ, PDB), advanced BLAST workflows, structures, phylogenetics. For quick BLAST, use gget. For direct REST API, use pubmed-database.
good-skill
Use when writing Python error handling code. Provides specific rules for exception chaining and custom error hierarchies.
mcp-builder
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
baostock
BaoStock is a free, open-source A-share securities data platform providing historical K-line, financial statements, macroeconomic indicators, and industry classification data via Python API, returning pandas DataFrames.
baostock
BaoStock is a free, open-source A-share securities data platform providing historical K-line, financial statements, macroeconomic indicators, and industry classification data via Python API, returning pandas DataFrames.
security-check
Scan Python projects for credential leaks, secrets in code, insecure patterns, LLM API key exposure, PII leakage to external AI services, and .env/.gitignore misconfigurations. Focused on data science pipelines handling API keys, tokens, and LLM integrations.
python
Apply these opinionated Python 3.11+ conventions when writing Python in this codebase: type hints with mypy, async/await, pytest fixtures, dataclasses, Poetry packaging, production patterns.
verification-loop
Unified verification engine for Python data science projects. Covers environment checks, type checking, linting, tests, security scans, code review with DS anti-patterns, and notebook checks. Commands (/verify, /quality-gate) invoke different subsets of this skill.
clean-code-guide
Helps you write better Python code by following clean code principles and software engineering best practices.
data-pipeline-patterns
Team conventions for Python data pipelines — stage structure, JSON output format, debugging workflow, and anti-patterns. Supplements standard patterns with team-specific rules.
python-conventions
Team-specific Python conventions — credential management with dotenv, API client rules, LLM response parsing, TDD workflow, and testing patterns for data pipelines.
openui-forge-python
OpenUI generative UI with Python FastAPI backend. OpenAI and Anthropic SDK variants.